Method and apparatus for intelligently recommending object

Abstract

Embodiments of the present disclosure provide a method and an apparatus for intelligently recommending an object, a device and a storage medium. The method includes: generating a user feature representation based on description information of a user request and a candidate object feature representation based on expertise information of a candidate object; determining a responsivity of the candidate object to the user based on the user feature representation and the candidate object feature representation; and selecting a target object for the user from candidate objects according to responsivities of the candidate objects to the user.

What is claimed is: 
     
         1 . A method for intelligently recommending an object, comprising:
 generating a user feature representation based on description information of a user request and a candidate object feature representation based on expertise information of a candidate object;   determining a responsivity of the candidate object to the user based on the user feature representation and the candidate object feature representation; and   selecting a target object for the user from candidate objects based on responsivities of the candidate objects to the user.   
     
     
         2 . The method of  claim 1 , wherein generating the user feature representation based on the description information of the user request and the candidate object feature representation based on the expertise information of the candidate object comprises:
 determining an entity comprised in the description information of the user request and an entity comprised in the expertise information of the candidate object; and   with an unsupervised vector generation model, generating the user feature representation based on the entity comprised in the description information of the user request and the candidate object feature representation based on the entity comprised in the expertise information of the candidate object.   
     
     
         3 . The method of  claim 2 , wherein, determining the entity comprised in the description information of the user request and the entity comprised in the expertise information of the candidate object comprises:
 performing word segmentation on the description information of the user request and the expertise information of the candidate object respectively based on entities in a knowledge graph of a field of the candidate object; and   inputting resultant word segments of the description information of the user request and the expertise information of the candidate object into a depth learning network model, to obtain the entity comprised in the description information of the user request and the entity comprised in the expertise information of the candidate object, wherein the depth learning network model comprises a bidirectional long-short term memory network layer, an attention mechanism layer, and a conditional random field layer.   
     
     
         4 . The method of  claim 2 , wherein the method further comprises:
 using literatures of a field of the candidate object as a corpus, to construct the unsupervised vector generation model.   
     
     
         5 . The method of  claim 2 , wherein, with the unsupervised vector generation model, generating the user feature representation based on the entity comprised in the description information of the user request and the candidate object feature representation based on the entity comprised in the expertise information of the candidate object comprises:
 with the unsupervised vector generation model, generating at least two user sub-feature representations based on at least two entities comprised in the description information of the user request as the user feature representation; and   with the unsupervised vector generation model, generating at least two object sub-feature representations based on at least two entities comprised in the expertise information of the candidate object; and averaging the at least two object sub-feature representations as the candidate object feature representation.   
     
     
         6 . The method of  claim 1 , wherein, determining the responsivity of the candidate object to the user based on the user feature representation and the candidate object feature representation comprises:
 determining the at least two user sub-feature representations in the user feature representation and at least two sub-responsivities in the candidate object feature representation; and   determining the responsivity of the candidate object to the user based on the at least two sub-responsivities.   
     
     
         7 . A device, comprising:
 one or more processors; and   a storage device, configured to store one or more programs,   wherein, when the one or more programs are executed by the one or more processors, the one or more processors are configured to:   generate a user feature representation based on description information of a user request and a candidate object feature representation based on expertise information of a candidate object;   determine a responsivity of the candidate object to the user based on the user feature representation and the candidate object feature representation; and   select a target object for the user from candidate objects based on responsivities of the candidate objects to the user.   
     
     
         8 . The device of  claim 7 , wherein the one or more processors are further configured to generate the user feature representation based on the description information of the user request and the candidate object feature representation based on the expertise information of the candidate object by:
 determining an entity comprised in the description information of the user request and an entity comprised in the expertise information of the candidate object; and   with an unsupervised vector generation model, generating the user feature representation based on the entity comprised in the description information of the user request and the candidate object feature representation based on the entity comprised in the expertise information of the candidate object   
     
     
         9 . The device of  claim 8 , wherein the one or more processors are configured to determine the entity comprised in the description information of the user request and the entity comprised in the expertise information of the candidate object by:
 performing word segmentation on the description information of the user request and the expertise information of the candidate object respectively based on entities in a knowledge graph of a field of the candidate object; and   inputting resultant word segments of the description information of the user request and the expertise information of the candidate object into a depth learning network model, to obtain the entity comprised in the description information of the user request and the entity comprised in the expertise information of the candidate object, wherein the depth learning network model comprises a bidirectional long-short term memory network layer, an attention mechanism layer, and a conditional random field layer.   
     
     
         10 . The device of  claim 8 , wherein the one or more processors are further configured to:
 use literatures of a field of the candidate object as a corpus, to construct the unsupervised vector generation model.   
     
     
         11 . The device of  claim 8 , wherein the one or more processors are configured to, with the unsupervised vector generation model, generate the user feature representation based on the entity comprised in the description information of the user request and the candidate object feature representation based on the entity comprised in the expertise information of the candidate object by:
 with the unsupervised vector generation model, generating at least two user sub-feature representations based on at least two entities comprised in the description information of the user request as the user feature representation; and   with the unsupervised vector generation model, generating at least two object sub-feature representations based on at least two entities comprised in the expertise information of the candidate object; and averaging the at least two object sub-feature representations as the candidate object feature representation.   
     
     
         12 . The device of  claim 7 , wherein the one or more processors are configured to determine the responsivity of the candidate object to the user based on the user feature representation and the candidate object feature representation by:
 determining the at least two user sub-feature representations in the user feature representation and at least two sub-responsivities in the candidate object feature representation; and   determining the responsivity of the candidate object to the user based on the at least two sub-responsivities.   
     
     
         13 . A non-transitory computer readable storage medium, having a computer program stored thereon, wherein, when the computer program is executed by a processor, a method for intelligently recommending an object, the method comprising:
 generating a user feature representation based on description information of a user request and a candidate object feature representation based on expertise information of a candidate object;   determining a responsivity of the candidate object to the user based on the user feature representation and the candidate object feature representation; and   selecting a target object for the user from candidate objects based on responsivities of the candidate objects to the user.   
     
     
         14 . The non-transitory computer readable storage medium of  claim 13 , wherein generating the user feature representation based on the description information of the user request and the candidate object feature representation based on the expertise information of the candidate object comprises:
 determining an entity comprised in the description information of the user request and an entity comprised in the expertise information of the candidate object; and   with an unsupervised vector generation model, generating the user feature representation based on the entity comprised in the description information of the user request and the candidate object feature representation based on the entity comprised in the expertise information of the candidate object.   
     
     
         15 . The non-transitory computer readable storage medium of  claim 14 , wherein, determining the entity comprised in the description information of the user request and the entity comprised in the expertise information of the candidate object comprises:
 performing word segmentation on the description information of the user request and the expertise information of the candidate object respectively based on entities in a knowledge graph of a field of the candidate object; and   inputting resultant word segments of the description information of the user request and the expertise information of the candidate object into a depth learning network model, to obtain the entity comprised in the description information of the user request and the entity comprised in the expertise information of the candidate object, wherein the depth learning network model comprises a bidirectional long-short term memory network layer, an attention mechanism layer, and a conditional random field layer.   
     
     
         16 . The non-transitory computer readable storage medium of  claim 14 , wherein the method further comprises:
 using literatures of a field of the candidate object as a corpus, to construct the unsupervised vector generation model.   
     
     
         17 . The non-transitory computer readable storage medium of  claim 14 , wherein, with the unsupervised vector generation model, generating the user feature representation based on the entity comprised in the description information of the user request and the candidate object feature representation based on the entity comprised in the expertise information of the candidate object comprises:
 with the unsupervised vector generation model, generating at least two user sub-feature representations based on at least two entities comprised in the description information of the user request as the user feature representation; and   with the unsupervised vector generation model, generating at least two object sub-feature representations based on at least two entities comprised in the expertise information of the candidate object; and averaging the at least two object sub-feature representations as the candidate object feature representation.   
     
     
         18 . The non-transitory computer readable storage medium of  claim 13 , wherein, determining the responsivity of the candidate object to the user based on the user feature representation and the candidate object feature representation comprises:
 determining the at least two user sub-feature representations in the user feature representation and at least two sub-responsivities in the candidate object feature representation; and   determining the responsivity of the candidate object to the user based on the at least two sub-responsivities.